[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …
promises significant advances to support the Sustainable Development Goals (SDGs). New …
Convolutional neural networks for multimodal remote sensing data classification
In recent years, enormous research has been made to improve the classification
performance of single-modal remote sensing (RS) data. However, with the ever-growing …
performance of single-modal remote sensing (RS) data. However, with the ever-growing …
Extended vision transformer (ExViT) for land use and land cover classification: A multimodal deep learning framework
The recent success of attention mechanism-driven deep models, like vision transformer (ViT)
as one of the most representatives, has intrigued a wave of advanced research to explore …
as one of the most representatives, has intrigued a wave of advanced research to explore …
Multimodal fusion transformer for remote sensing image classification
Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …
promising performance when compared with convolutional neural networks (CNNs). As a …
Hyperspectral and LiDAR data classification based on structural optimization transmission
With the development of the sensor technology, complementary data of different sources can
be easily obtained for various applications. Despite the availability of adequate multisource …
be easily obtained for various applications. Despite the availability of adequate multisource …
[HTML][HTML] Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model
As remote sensing (RS) data obtained from different sensors become available largely and
openly, multimodal data processing and analysis techniques have been garnering …
openly, multimodal data processing and analysis techniques have been garnering …
Hyperspectral image classification with attention-aided CNNs
Convolutional neural networks (CNNs) have been widely used for hyperspectral image
classification. As a common process, small cubes are first cropped from the hyperspectral …
classification. As a common process, small cubes are first cropped from the hyperspectral …
Deep hierarchical vision transformer for hyperspectral and LiDAR data classification
Z Xue, X Tan, X Yu, B Liu, A Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture
for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current …
for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current …
Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks
S Ghaderizadeh, D Abbasi-Moghadam… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Due to the unique feature of the three-dimensional convolution neural network, it is used in
image classification. There are some problems such as noise, lack of labeled samples, the …
image classification. There are some problems such as noise, lack of labeled samples, the …